Lucky 7 — GPU System Wins Student Supercomputing Competition Again

For the seventh time in a row, a GPU based system powered the winning team in a student cluster competition.

This time, it was the team from Shanghai Jiao Tong University (SJTU) taking the top spot in the largest student supercomputer challenge, ASC14, held last month. In fact, the top three teams all relied on NVIDIA GPUs.

Winning team: students from China’s Shanghai Jiao Tong University.

And the new NVIDIA K40 GPUs helped China’s Sun Yat-sen University set a new record. The team got a record 9.27 teraflops on the HPC industry standard Linpack performance benchmark. The previous record: 8.45 teraflops.

Winning Contests and Grooming New HPC Pros

Armed with a student-built cluster powered by eight NVIDIA K20 GPU accelerators, SJTU earned the highest total score for performance on six tests.

These included runs on the elastic wave modeling application, 3D-EW; a run on quantum chemistry application, Quantum ESPRESSO; and other real-world scientific codes.

Our Simon See, who also serves as an adjunct professor at SJTU, sat down with their team lead, James Lin, vice director Center for HPC, to get his thoughts on GPUs:

See: Why are events like the Student Supercomputing Challenge important?

Lin: We need to educate new legions of students in high-performance computing. Teaching HPC skills in a competition like this can be more effective than in a classroom. In fact, as a result of his experience, one of our team members decided to focus on HPC PhD at Virginia Tech summer.

See: How did you and your team prepare for the competition?

Lin: We spent more than four months preparing for the contest, even over the Chinese Spring Festival. I first asked the team to study course material on HPC, and provided additional detail in my lectures. In phase two, the team practiced running code on “π,” SJTU’s supercomputer with 100 NVIDIA Tesla K20 GPUs. Finally, the team reviewed competition source codes and optimized them using many difference approaches.

See: What was the most challenging aspect of the competition from your perspective?

Lin: All of my students are from the computer science department, so they knew very little about the background of scientific applications, like Quantum ESPRESSO, before the contest. Fortunately, some of the π users are experienced with these applications, so they were able to help. In the end, we received the top score for three of the five applications.

See: Aside from the competition, are you using GPUs in any important aspects of your research? If so, how are they impacting your work?

Lin: Yes, GPUs are important to our HPC work at SJTU. Half of the computational power of π is from NVIDIA GPUs, and they been used quite extensively since π was deployed. Also, GPUs allow us to accelerate our research, primarily with molecular dynamics applications today, but we hope to also utilize them for machine learning research in the future.

James and the SJTU team aren’t planning on taking a break anytime soon.